code: def get_eval_metric(algorithm_name, ml_task, automl_eval_metric):
      if algorithm_name == "Xgboost":
          return xgboost_eval_metric(ml_task, automl_eval_metric)
  
      return automl_eval_metric
  
file_path: mljar-supervised/supervised/algorithms/registry.py
imports: import supervised.algorithms.baseline
  import supervised.algorithms.catboost
  import supervised.algorithms.decision_tree
  import supervised.algorithms.extra_trees
  import supervised.algorithms.knn
  import supervised.algorithms.lightgbm
  import supervised.algorithms.linear
  import supervised.algorithms.nn
  import supervised.algorithms.random_forest
  import supervised.algorithms.xgboost
label_desc: Use image node_id 7 for calling the AlgorithmsRegistry obj's underlying member method code with example usage: obj.get_eval_metric(algorithm_name, ml_task, automl_eval_metric) and returns: automl_eval_metric, xgboost_eval_metric